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http://dx.doi.org/10.6109/jkiice.2021.25.10.1317

Iterative Low Rank Approximation for Image Denoising  

Kim, Seehyun (Department of Information and Communications Engineering, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.10, 2021 , pp. 1317-1322 More about this Journal
Abstract
Nonlocal similarity of natural images leads to the fact that a patch matrix whose columns are similar patches of the reference patch has a low rank. Images corrupted by additive white Gaussian noises (AWGN) make their patch matrices to have a higher rank. The noise in the image can be reduced by obtaining low rank approximation of the patch matrices. In this paper, an image denoising algorithm is proposed, which first constructs the patch matrices by combining the similar patches of each reference patch, which is a part of the noisy image. For each patch matrix, the proposed algorithm calculates its low rank approximate, and then recovers the original image by aggregating the low rank estimates. The simulation results using widely accepted test images show that the proposed denoising algorithm outperforms four recent methods.
Keywords
Denoising; Nonlocal similarity; Low rank; Matrix approximation; Weighted patch matrix;
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